Measurement-based quantum computing uses measurement patterns on predefined quantum resource states to execute quantum logic. Quantum simulation offers an important use case on near-term devices. However, pattern optimization depends on the multivariable interplay between hardware and software constraints and is therefore use-dependent and highly non-trivial. Optimization of large-scale patterns under realistic assumptions remains a barrier. We announce the release of the quantum measurement pattern library QPatLib, a dataset that, in v1.0, presents patterns for use in measurement-based quantum simulation. We present the workflow for generating patterns that execute Pauli-string unitaries needed for many quantum algorithms. We provide benchmark patterns for measurement-based quantum unitary evolution. The measurement patterns are defined with different conventions for commuting Pauli-string subsets to allow scaling of pattern size and complexity. The purpose of the library is to (i) serve as a standardized testbed for pattern-optimization protocols for measurement-based quantum simulation routines, (ii) offer a suite of patterns for direct use on hardware, (iii) provide data to empirically justify pattern design principles, and (iv) provide a flexible resource for future storage and use of measurement-based patterns beyond quantum simulation.
Measurement-based quantum computing uses measurement patterns on predefined quantum resource states to execute quantum logic. Quantum simulation offers an important use case on near-term devices. However, pattern optimization depends on the multivariable interplay between hardware and software constraints and is therefore use-dependent and highly non-trivial. Optimization of large-scale patterns under realistic assumptions remains a barrier. We announce the release of the quantum measurement pattern library QPatLib, a dataset that, in v1.0, presents patterns for use in measurement-based quantum simulation. We present the workflow for generating patterns that execute Pauli-string unitaries needed for many quantum algorithms. We provide benchmark patterns for measurement-based quantum unitary evolution. The measurement patterns are defined with different conventions for commuting Pauli-string subsets to allow scaling of pattern size and complexity. The purpose of the library is to (i) serve as a standardized testbed for pattern-optimization protocols for measurement-based quantum simulation routines, (ii) offer a suite of patterns for direct use on hardware, (iii) provide data to empirically justify pattern design principles, and (iv) provide a flexible resource for future storage and use of measurement-based patterns beyond quantum simulation.
We introduce an open-source software library Graphix, which optimizes and simulates measurement-based quantum computation (MBQC). By combining the measurement calculus with an efficient graph state simulator, Graphix allows the classical preprocessing of Pauli measurements in the measurement patterns, significantly reducing the number of operations required to perform the quantum computation while maintaining determinism. For a measurement pattern translated from a quantum circuit, this corresponds to the preprocessing of all Clifford gates, and this improvement in the one-way model is important for efficient operations in quantum hardware with limited qubit numbers. In addition to the direct translation from gate networks, we provide a pattern generation method based on flow-finding algorithms, which automatically generates byproduct correction sequences to ensure determinism. We further implement optimization strategies for measurement patterns beyond the standardization procedure and provide tensor-network backend for classically simulating the MBQC.
Reproducibility and comparability remain fundamental challenges in the empirical evaluation of Quantum Software Engineering (QSE) tools. These challenges arise from the intrinsic probabilistic behavior of quantum programs and the variability introduced by quantum hardware and simulator backends. While existing quantum benchmarks typically offer only static circuit definitions, they often leave researchers responsible for data generation and execution, hindering the creation of standardized and reproducible experiments. In this paper, we introduce QSimBench, a novel, open-source benchmark suite and accompanying Python library designed to address these limitations. QSimBench provides over 20 million precomputed shot-level execution traces:20,000measurements each, across14quantum algorithms, multiple circuit sizes (4−15qubits), and six Qiskit-based backends including both ideal and noise-injected simulations. Each configuration includes not only detailed measurement histories but also the original circuit in OpenQASM, the backend's noise model, and configuration metadata, ensuring full transparency and enabling in-depth analysis. QSimBench empowers researchers to conduct scalable, reproducible, and statistically rigorous evaluations of QSE tools, such as those for monitoring, orchestration, and statistical post-processing, without the need for expensive or difficult-to-reproduce quantum executions. The benchmark is distributed with a lightweight Python API that facilitates fast data retrieval, flexible sampling strategies, and seamless integration into experimental pipelines. QSimBench is publicly available, aiming to foster collaborative and reproducible research within the quantum software community.
Abstract The paradigm of measurement-based quantum computing (MBQC) starts from a highly entangled resource state on which unitary operations are executed through adaptive measurements and corrections ensuring determinism. This is set in contrast to the more common quantum circuit model, in which unitary operations are directly implemented through quantum gates prior to final measurements. In this work, we incorporate concepts from MBQC into the circuit model to create a hybrid simulation technique, permitting us to split any quantum circuit into a classically efficiently simulatable Clifford-part and a second part consisting of a stabilizer state and local (adaptive) measurement instructions—a so-called standard form—which is executed on a quantum computer. We further process the stabilizer state with the graph state formalism, thus, enabling a significant decrease in circuit depth for certain applications. We show that groups of mutually-commuting operators can be implemented using fully-parallel, i.e. non-adaptive, measurements within our protocol. In addition, we discuss how groups of mutually commuting observables can be simulatenously measured by adjusting the resource state, rather than performing a costly basis transformation prior to the measurement as it is done in the circuit model. Finally, we demonstrate the utility of our technique on two examples of high practical relevance—the Quantum Approximate Optimization Algorithm and the Variational Quantum Eigensolver (VQE) for the ground-state energy estimation of the water molecule. For the VQE, we find a reduction of the depth by a factor of 4 to 5 using measurement patterns vs. the standard circuit model. At the same time, since we incorporate the simultaneous measurements, our patterns allow us to save shots by a factor of at least 3.5 compared to measuring Pauli strings individually in the circuit model.
In the near-term "NISQ"-era of noisy, intermediate-scale, quantum hardware and beyond, reliably determining the quality of quantum devices becomes increasingly important: users need to be able to compare them with one another, and make an estimate whether they are capable of performing a given task ahead of time. In this work, we develop and release an advanced quantum benchmarking framework in order to help assess the state of the art of current quantum devices. Our testing framework measures the performance of universal quantum devices in a hardware-agnostic way, with metrics that are aimed to facilitate an intuitive understanding of which device is likely to outperform others on a given task. This is achieved through six structured tests that allow for an immediate, visual assessment of how devices compare. Each test is designed with scalability in mind, making this framework not only suitable for testing the performance of present-day quantum devices, but also of those released in the foreseeable future. The series of tests are motivated by real-life scenarios, and therefore emphasise the interplay between various relevant characteristics of quantum devices, such as qubit count, connectivity, and gate and measurement fidelity. We present the benchmark results of twenty-one different quantum devices from IBM, Rigetti and IonQ.
As quantum processors continue to scale in size and complexity, the need for well-defined, reproducible, and technology-agnostic performance metrics becomes increasingly critical. Here we present a suite of scalable quantum computing benchmarks developed as key performance indicators (KPIs) within the EU Quantum Flagship. These proposed benchmarks are designed to assess holistic system performance rather than isolated components, and to remain applicable across both noisy intermediate-scale quantum (NISQ) devices and future fault-tolerant architectures. We introduce four core benchmarks addressing complementary aspects of quantum computing capability: large multi-qubit circuit execution via a Clifford Volume benchmark, scalable multipartite entanglement generation through GHZ-state preparation, a benchmark based on the application of Shor's period-finding subroutine to simple functions, and a protocol quantifying the benefit of quantum error correction using Bell states. Each benchmark is accompanied by clearly specified protocols, reporting standards, and scalable evaluation methods. Together, these KPIs provide a coherent framework for transparent and fair performance assessment across quantum hardware platforms and for tracking progress late-NISQ toward early fault-tolerant quantum computation.
Quantum Hamiltonian simulation is one of the most promising applications of quantum computing and forms the basis for many quantum algorithms. Benchmarking them is an important gauge of progress in quantum computing technology. We present a methodology and software framework to evaluate various facets of the performance of gate-based quantum computers on Trotterized quantum Hamiltonian evolution. We propose three distinct modes for benchmarking: (i) comparing simulation on a real device to that on a noiseless classical simulator, (ii) comparing simulation on a real device with exact diagonalization results, and (iii) using scalable mirror circuit techniques to assess hardware performance in scenarios beyond classical simulation methods. We demonstrate this framework on five Hamiltonian models from the HamLib library: the Fermi and Bose-Hubbard models, the transverse field Ising model, the Heisenberg model, and the Max3SAT problem. Experiments were conducted using Qiskit's Aer simulator, BlueQubit's CPU cluster and GPU simulators, and IBM's quantum hardware. Our framework, extendable to other Hamiltonians, provides comprehensive performance profiles that reveal hardware and algorithmic limitations and measure both fidelity and execution times, identifying crossover points where quantum hardware outperforms CPU/GPU simulators.